The situation
Rayna Tours planned to use AI in customer service and operations: chat assistance, recommendations, itinerary generation and document processing. The business first needed to choose its platforms, and the engineering team needed a safe place to run an AI agent service.
What we did
- Wrote an AI platform decision guide that compared Azure AI, Google Cloud AI, the OpenAI API and AWS for the tourism use cases. It set clear principles: treat AI services as stateless APIs, avoid early lock-in, centralise prompt versioning and logging, and enforce data protection, rate limits and auditability.
- Built the hosting for the AI agent service on the existing AWS platform: a container service behind the shared load balancer, a private PostgreSQL database and a private cache, wired in through the same parameter contracts as every other service.
- Kept the same guardrails as the rest of the estate: keyless pipelines, private networking and explicit egress rules.
Why it matters
The AI service did not need a special path to production. It went through the same reviewed pipeline and guardrails as everything else. That is the point: AI workloads should inherit the platform’s controls, not bypass them.